MODIFIKASI ALGORITMA J-BIT ENCODING UNTUK MENINGKATKAN RASIO KOMPRESI
Bibliographic record
Abstract
J-bit encoding merupakan algoritma kompresi lossless yang memanipulasi \nsetiap bit data dalam file untuk meminimalkan ukuran, dengan cara membagi data \nmenjadi dua output kemudian dikombinasikan kembali menjadi satu output. \nPenelitian ini mengusulkan modifikasi algoritma J-bit encoding dengan cara \nmengeliminasi simbol nol dan satu dari output pertama, sehingga output pertama \nakan berisi data asli selain nol dan satu (dalam ukuran byte) dan output kedua akan \nberisi nilai dua bit yang menjelaskan posisi byte nol, byte satu, dan byte selain nol \ndan satu. Perbandingan kedua algoritma ini dilakukan dengan menguji empat skema \nkombinasi algoritma yaitu (i) transformasi Burrows-Wheeler, Move to Front, J-bit \nencoding dan pengkodean aritmatika, (ii) transformasi Burrows-Wheeler, Move to \nFront, algoritma hasil modifikasi dan pengkodean aritmatika, (iii) transformasi \nBurrows-Wheeler, Move One From Front, J-bit encoding dan pengkodean \naritmatika, (iv) transformasi Burrows-Wheeler, Move One From Front, algoritma \nhasil modifikasi dan pengkodean aritmatika. Dengan menggunakan dataset calgary \ncorpus dan canterbury corpus, hasil pengujian menunjukan bahwa rata-rata rasio \nkompresi terbaik diperoleh dengan menggunakan skema kedua. Selain efektif, \nalgoritma hasil modifikasi juga lebih efisien dibandingkan dengan algoritma J-bit \nencoding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".